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DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing
22,726 datasets
A report from the Australian Ocean Data Network describes the iterative methodology for creating seascape classifications for Australia's marine region. The process uses an unsupervised 'crisp' ISOClass classification in ERMapper, combining biophysical properties with consistent relationships to benthic biota. An initial validation of the classification technique was performed on a subset of data for the shelf surrounding Tasmania using an alternative 'fuzzy' classification.
A 44.0 KB PDF file contains data from a study on human umbilical cord mesenchymal stem cell–derived exosomes (hUCMSC-Exos) in an IgA nephropathy-like mouse model. The data likely contains measurements of renal injury markers, gut microbiota composition from 16S rRNA sequencing, and inflammatory signaling markers. The dataset was authored by Yuanyuan He and last updated on 2026-06-01.
1,294 co-dysregulated genes were identified linking insomnia and sepsis-induced acute lung injury (SALI). Machine learning analysis prioritized three hub genes, with PTPN6 validated as a key biomarker. The dataset, authored by Jinquan Zhang and last updated in 2026, supports research into the molecular mechanisms of this condition.
Yuanyuan He published data on figshare in June 2026 under a CC-BY-4.0 license. The dataset likely contains measurements from a study of human umbilical cord mesenchymal stem cell–derived exosomes in an IgA nephropathy-like mouse model. It includes assessments of renal function, histopathology, systemic inflammatory markers, and gut microbiota composition analyzed via 16S rRNA sequencing.
621.1 KB of multi-omics data identifies genes linking insomnia to sepsis-induced acute lung injury. The dataset, authored by Jinquan Zhang and last updated in 2026, results from Mendelian randomization, WGCNA, and machine learning analysis. It includes 1,294 co-dysregulated genes and three prioritized hub genes validated via single-cell RNA sequencing and in vivo experiments.
A Mendelian randomization analysis identified insomnia as a causal determinant for sepsis susceptibility. This 5.8 MB dataset, authored by Jinquan Zhang and last updated in June 2026, integrates multi-omics approaches and machine learning to identify hub genes linking insomnia to acute lung injury. Machine learning techniques, including Random Forest, SVM, and KNN, were used to refine a signature of 1,294 co-dysregulated genes down to three robust candidates.
Shuaijie Pei's dataset on figshare contains bioinformatics and machine learning analysis results for sepsis-related cellular senescence. It identifies eight differentially expressed senescence-related genes and potential targeted drugs from the DSigDB database. The dataset is 123.5 MB in size and was last updated on June 1, 2026.
A figshare-hosted dataset by Qingqing Si, last updated in May 2026, presents results from an integrative Post-GWAS analysis of vitiligo. The 29.8 MB Excel file contains findings on the genetic architecture differences between early-onset and late-onset vitiligo subtypes, including genetic correlation, cell-type enrichment, and identified core target genes.
New South Wales land parcel and property data representing unidentified parcels, including Crown land, vested land, and Old System lots. The dataset is part of the Foundational Spatial Data Framework, updated to the GDA2020 national standard by Spatial Services (DCS). The service was last updated on 2026-05-22.
Infant stool samples from 0-12 months and breast milk samples from 1-30 days postpartum were collected from 69 mother-infant dyads in rural and urban Manitoba. The dataset, authored by Saeid Khakisahneh and last updated in May 2026, includes results from 16S rRNA sequencing and gas chromatography–mass spectrometry analyses of microbiota and short-chain fatty acids. It reveals distinct microbial patterns and metabolite levels associated with geographical residency.
A 28.6 GB dataset from a 2026 study investigating a method to quantify live Shiga-toxin-producing Escherichia coli (STEC) in ground beef. The research, authored by Katrina Counihan, combined serial plating with long-read sequencing to identify virulence genes and quantify STEC down to 1 colony-forming unit per gram. Data files are in POD5 and GZ formats, representing raw sequencing outputs from single bacterial colonies.
A 2026 survey of 321 respondents during a live autonomous shuttle trial at the National Exhibition Centre campus in Birmingham, UK. The study, authored by Hisham Y. Makahleh, investigated public awareness, user experience, and behavioral intentions, finding that 62% of users would 'definitely' use the shuttle regularly if available.
Hisham Y. Makahleh provides survey data from a live autonomous shuttle trial at the National Exhibition Centre in Birmingham, UK. The dataset includes responses from 321 participants, with 51% aware of the service and 71% of those aware having used the shuttle. It was last updated on 2026-05-17.
321 survey responses from a public trial of an autonomous shuttle at the National Exhibition Centre campus in Birmingham, UK. The data includes evaluations of ten service attributes and was collected by Hisham Y. Makahleh in 2026. Principal component analysis identified a single underlying dimension of 'overall passenger experience'.
A metabolomics study of a non-tannin sorghum recombinant inbred line population of 189 individuals plus two parents, analyzed using LC-MS. The dataset likely contains metabolite profiles linked to antimicrobial activity against Clostridium perfringens, measured via minimum inhibitory concentration and qPCR assays. The data was authored by María Antonella Conti and last updated on 2026-05-11.
A predictive model for infection risk in chronic nonhealing wounds developed using electronic medical record data. The model was built on a primary cohort of 500 patients from two tertiary hospitals in China and validated on an external cohort of 300 patients. It was authored by Yang Jiang and last updated in May 2026.
Six consensus predictors—smoking, diabetes duration, wound depth, elevated C-reactive protein, elevated procalcitonin, and hypoalbuminemia—were used to build a random forest model predicting infection risk in chronic wounds. The model was developed using a primary cohort of 500 patients from two tertiary hospitals in China and validated on an external cohort of 300 patients. Yang Jiang published this research on figshare in May 2026, deploying the model as a free web calculator for clinical use.
Yang Jiang developed a random forest model to predict infection risk in chronic nonhealing wounds using routine electronic medical record data. The model was trained on a primary cohort of 500 patients from two tertiary hospitals in China and validated on an external cohort of 300 patients. It was last updated on May 19, 2026.
A dataset supporting a predictive model for infection risk in chronic nonhealing wounds, developed using routine electronic medical record data. The study involved a primary cohort of 500 patients split into training and testing sets and an external validation cohort of 300 patients from two tertiary hospitals in China. The model was developed by Yang Jiang and published on figshare in May 2026.
500 patient records from a primary cohort and 300 from an external validation cohort were used to develop a random forest model predicting infection risk in chronic wounds. The model, created by Yang Jiang and published on figshare in 2026, uses six predictors derived from routine electronic medical record data. It achieved an AUROC of 0.884 in testing and 0.855 in external validation.